Indicator and analytics for sensor insertion in a continuous analyte monitoring system and related methods

ABSTRACT

The present embodiments provide systems and methods for, among others, tracking sensor insertion locations in a continuous analyte monitoring system. Data gathered from sensor sessions can be used in different ways, such as providing a user with a suggested rotation of insertion locations, correlating data from a given sensor session with sensor accuracy and/or sensor session length, and providing a user with a suggested next insertion location based upon past sensor accuracy and/or sensor session length at that location.

INCORPORATION BY REFERENCE TO RELATED APPLICATION

Any and all priority claims identified in the Application Data Sheet, orany correction thereto, are hereby incorporated by reference under 37CFR 1.57. This application is a continuation of U.S. application Ser.No. 14/539,890, filed Nov. 12, 2014, which claims the benefit of U.S.Provisional Application No. 61/904,396, filed Nov. 14, 2013. Each of theaforementioned applications is incorporated by reference herein in itsentirety, and is hereby expressly made a part of this specification.

TECHNICAL FIELD

The present embodiments relate to continuous analyte monitoring, and, inparticular, to placement of a sensor of a continuous analyte monitoringsystem on the body of the user.

BACKGROUND

Diabetes mellitus is a disorder in which the pancreas cannot createsufficient insulin (Type I or insulin dependent) and/or in which insulinis not effective (Type 2 or non-insulin dependent). In the diabeticstate, the victim suffers from high blood sugar, which can cause anarray of physiological derangements associated with the deterioration ofsmall blood vessels, for example, kidney failure, skin ulcers, orbleeding into the vitreous of the eye. A hypoglycemic reaction (lowblood sugar) can be induced by an inadvertent overdose of insulin, orafter a normal dose of insulin or glucose-lowering agent accompanied byextraordinary exercise or insufficient food intake.

Conventionally, a person with diabetes carries a self-monitoring bloodglucose (SMBG) monitor, which typically requires uncomfortable fingerpricking methods. Due to the lack of comfort and convenience, a personwith diabetes normally only measures his or her glucose levels two tofour times per day. Unfortunately, such time intervals are so far spreadapart that the person with diabetes likely finds out too late of ahyperglycemic or hypoglycemic condition, sometimes incurring dangerousside effects. Glucose levels may be alternatively monitored continuouslyby a sensor system including an on-skin sensor assembly. The sensorsystem may have a wireless transmitter that transmits measurement datato a receiver that processes and displays information based on themeasurements. Such sensor systems are sometimes referred to ascontinuous glucose monitors (CGMs).

SUMMARY

The present embodiments have several features, no single one of which issolely responsible for their desirable attributes. Without limiting thescope of the present embodiments as expressed by the claims that follow,their more prominent features now will be discussed briefly. Afterconsidering this discussion, and particularly after reading the sectionentitled “Detailed Description,” one will understand how the features ofthe present embodiments provide the advantages described herein.

A sensor of a CGM system is implanted through the skin of the host. Whenthe sensor has reached the limit of its lifespan, it is removed and anew sensor is inserted. A typical sensor lifespan is anywhere from a fewdays to one week or more. Users of CGM systems thus insert a new sensorsometimes as often as every few days. Because the sensor penetrates theskin, repeated sensor insertion in the same location can lead toscarring. Thus, it can be desirable to vary the location for each newsensor insertion. However, the effectiveness of the sensor may beimpacted by the location where it is implanted, because the compositionof the host's body varies from one location to another, includingfactors such as fat concentration, skin thickness, presence ofcapillaries, muscle tissue, body heat, skin perspiration at the site,scarring, thickness of skin, blood flow, movement of the location,vascularization, muscle movement, exposure to externalelements/temperature (e.g., wrist vs. abdomen), insulin pump infusionsites, tattoos, rash or other skin conditions such as eczema orpsoriasis Thus, it would be desirable to track information related tosensor insertion site location, for example, to know what location(s) ona given host's body are most likely to achieve the best CGM results.

In a first aspect, which is generally applicable (i.e. independentlycombinable with any of the aspects or embodiments identified herein),particularly with any other embodiment of the first aspect, certain ofthe present embodiments comprise a method for continuous analytemonitoring including a continuous analyte monitoring system having asensor. The method comprises initiating a new sensor session with atransmitter and a device having a display. The method further comprisesdisplaying a diagram of a body on the display. The method furthercomprises receiving as an input, via the diagram, a location on the bodywhere the sensor was inserted into skin of a host. The method furthercomprises storing the location.

In an embodiment of the first aspect, the method further comprisescorrelating data from the sensor for the sensor session with thelocation.

In an embodiment of the first aspect, the method further comprisesstoring the correlated data.

In an embodiment of the first aspect, the method further comprisestransmitting the correlated data to a database, the database includingother correlated data associated with other hosts.

In an embodiment of the first aspect, the data includes quantitativedata regarding one or more sensor sessions corresponding to one or moresensor insertion locations.

In an embodiment of the first aspect, the quantitative data comprises atleast one of sensor accuracy, sensor session length, sensor baseline,sensor sensitivity, sensor sensitivity decline over time, sensorperformance vs. past performance in a same person, sensor performancevs. a population of users, sensor performance by days or wear, sensorperformance by geographical location, sensor performance by externalenvironment, sensor performance by activity level, data indicative ofsignal noise, time spent out of communication range, adhesive data,reliability, data capture, noise metrics, detected faults, end of lifemetrics, and confidence levels.

In an embodiment of the first aspect, the method further comprisesreceiving as an input personal information of the host.

In an embodiment of the first aspect, the personal information includesat least one of height, weight, age, sex, body mass index (BMI), theuser's current mood, the user's current pain level, the user's currentcomfort level, the user's current confidence level, the user'sperception of sensor performance, a location of an insulin infusion pumprelative to the sensor, adhesive irritation, or adhesive success rate.

In a second aspect, which is generally applicable (i.e. independentlycombinable with any of the aspects or embodiments identified herein),particularly with any other embodiment of the second aspect, certain ofthe present embodiments comprise a method for continuous analytemonitoring including a continuous analyte monitoring system having asensor. The method comprises initiating a new sensor session with atransmitter and a device having a display and a camera. The methodfurther comprises receiving as an input, via the camera, a photograph ofa location on a body where the sensor was inserted into skin of a host.The method further comprises analyzing the photograph to determine thelocation. The method further comprises storing the location.

In an embodiment of the second aspect, the method further comprisescorrelating data from the sensor for the sensor session with thelocation.

In an embodiment of the second aspect, the method further comprisesstoring the correlated data.

In an embodiment of the second aspect, the method further comprisestransmitting the correlated data to a database, the database includingother correlated data associated with other hosts.

In an embodiment of the second aspect, the data includes quantitativedata regarding one or more sensor sessions corresponding to one or moresensor insertion locations.

In an embodiment of the second aspect, the quantitative data comprisesat least one of sensor accuracy, sensor session length, sensor baseline,sensor sensitivity, sensor sensitivity decline over time, sensorperformance vs. past performance in a same person, sensor performancevs. a population of users, sensor performance by days or wear, sensorperformance by geographical location, sensor performance by externalenvironment, sensor performance by activity level, data indicative ofsignal noise, time spent out of communication range, adhesive data,reliability, data capture, noise metrics, detected faults, end of lifemetrics, and confidence levels.

In an embodiment of the second aspect, the method further comprisesreceiving as an input personal information of the host.

In an embodiment of the second aspect, the personal information includesat least one of height, weight, age, sex, body mass index (BMI), theuser's current mood, the user's current pain level, the user's currentcomfort level, the user's current confidence level, the user'sperception of sensor performance, a location of an insulin infusion pumprelative to the sensor, adhesive irritation, or adhesive success rate.

In a third aspect, which is generally applicable (i.e. independentlycombinable with any of the aspects or embodiments identified herein),particularly with any other embodiment of the third aspect, certain ofthe present embodiments comprise a method for continuous analytemonitoring. The method comprises displaying, on a device having adisplay, a diagram of a body, the diagram indicating at least onelocation on the body where a sensor of a continuous analyte monitoringsystem was previously inserted. The method further comprises displaying,on the display, information about each location.

In an embodiment of the third aspect, the information includes a datewhen the sensor was inserted at each location.

In an embodiment of the third aspect, the information includesquantitative data regarding one or more sensor sessions associated witheach location.

In an embodiment of the third aspect, the quantitative data comprises atleast one of sensor accuracy, sensor session length, sensor baseline,sensor sensitivity, sensor sensitivity decline over time, sensorperformance vs. past performance in a same person, sensor performancevs. a population of users, sensor performance by days or wear, sensorperformance by geographical location, sensor performance by externalenvironment, sensor performance by activity level, data indicative ofsignal noise, time spent out of communication range, adhesive data,reliability, data capture, noise metrics, detected faults, end of lifemetrics, and confidence levels.

In a fourth aspect, which is generally applicable (i.e. independentlycombinable with any of the aspects or embodiments identified herein),particularly with any other embodiment of the fourth aspect, certain ofthe present embodiments comprise a method for continuous analytemonitoring. The method comprises downloading, to a device having adisplay, data regarding one or more sensor sessions associated with aplurality of hosts, the data being correlated with a location of each ofthe sensor sessions. The method further comprises displaying, on thedisplay, the correlated data.

In an embodiment of the fourth aspect, the method further comprisesproviding a suggested sensor insertion location on a body of a host,based on the correlated data.

In an embodiment of the fourth aspect, the correlated data comprises atleast one of sensor accuracy, sensor session length, sensor baseline,sensor sensitivity, sensor sensitivity decline over time, sensorperformance vs. past performance in a same person, sensor performancevs. a population of users, sensor performance by days or wear, sensorperformance by geographical location, sensor performance by externalenvironment, sensor performance by activity level, data indicative ofsignal noise, time spent out of communication range, adhesive data,reliability, data capture, noise metrics, detected faults, end of lifemetrics, and confidence levels correlated with each location.

In a fifth aspect, which is generally applicable (i.e. independentlycombinable with any of the aspects or embodiments identified herein),particularly with any other embodiment of the fifth aspect, certain ofthe present embodiments comprise a system for continuous analytemonitoring. The system comprises an electronic device having a displayand storing executable instructions. The system further comprises acontinuous analyte sensor configured to be implanted within a body. Thesystem further comprises sensor electronics configured to receive andprocess sensor data output by the sensor, to initialize the sensor, andto transmit a signal to the electronic device. Upon insertion of thesensor into skin of the host, a new sensor session is initiated with thesensor electronics and the electronic device, and the electronic deviceexecutes the executable instructions to display a diagram of a body onthe display.

In an embodiment of the fifth aspect, the electronic device isconfigured to receive as an input, via the diagram, a location on thebody where the sensor was inserted.

In an embodiment of the fifth aspect, the electronic device isconfigured to store the location.

In an embodiment of the fifth aspect, the electronic device isconfigured to correlate data from the sensor for the sensor session withthe location.

In an embodiment of the fifth aspect, the electronic device isconfigured to store the correlated data.

In an embodiment of the fifth aspect, the electronic device isconfigured to transmit the correlated data to a database, the databaseincluding other correlated data associated with other hosts.

In an embodiment of the fifth aspect, the electronic device isconfigured to receive as an input personal information of the host.

In an embodiment of the fifth aspect, the personal information includesat least one of height, weight, age, sex, body mass index (BMI), theuser's current mood, the user's current pain level, the user's currentcomfort level, the user's current confidence level, the user'sperception of sensor performance, a location of an insulin infusion pumprelative to the sensor, adhesive irritation, or adhesive success rate.

In an embodiment of the fifth aspect, the electronic device is asmartphone.

In an embodiment of the fifth aspect, the executable instructionscomprise a downloadable application.

In a sixth aspect, which is generally applicable (i.e. independentlycombinable with any of the aspects or embodiments identified herein),particularly with any other embodiment of the sixth aspect, certain ofthe present embodiments comprise a system for continuous analytemonitoring. The system comprises an electronic device having a displayand a camera, and storing executable instructions. The system furthercomprises a continuous analyte sensor configured to be implanted withina body. The system further comprises sensor electronics configured toreceive and process sensor data output by the sensor, to initialize thesensor, and to transmit a signal to the electronic device. Uponinsertion of the sensor into skin of a host, a new sensor session isinitiated with the sensor electronics and the electronic device. Theelectronic device receives as an input, via the camera, a photograph ofa location on the body where the sensor was inserted into the skin ofthe host, and the electronic device stores the location.

In an embodiment of the sixth aspect, the electronic device isconfigured to correlate data from the sensor for the sensor session withthe location.

In an embodiment of the sixth aspect, the electronic device isconfigured to store the correlated data.

In an embodiment of the sixth aspect, the electronic device isconfigured to transmit the correlated data to a database, the databaseincluding other correlated data associated with other hosts.

In an embodiment of the sixth aspect, the electronic device isconfigured to receive as an input personal information of the host.

In an embodiment of the sixth aspect, the personal information includesat least one of height, weight, age, sex, body mass index (BMI), theuser's current mood, the user's current pain level, the user's currentcomfort level, the user's current confidence level, the user'sperception of sensor performance, a location of an insulin infusion pumprelative to the sensor, adhesive irritation, or adhesive success rate.

In an embodiment of the sixth aspect, the electronic device is asmartphone.

In an embodiment of the sixth aspect, the executable instructionscomprise a downloadable application.

In a seventh aspect, which is generally applicable (i.e. independentlycombinable with any of the aspects or embodiments identified herein),particularly with any other embodiment of the seventh aspect, certain ofthe present embodiments comprise a method for continuous analytemonitoring. The method comprises displaying, on a device having adisplay, a diagram of a body. The method further comprises indicating,on the diagram, a recommended location for insertion of a sensor.

In an embodiment of the seventh aspect, the method further comprisesperforming a pattern analysis of data relating to previous sensorinsertion locations.

In an embodiment of the seventh aspect, the data includes datapertaining to at least one other user.

In an eighth aspect, which is generally applicable (i.e. independentlycombinable with any of the aspects or embodiments identified herein),particularly with any other embodiment of the eighth aspect, certain ofthe present embodiments comprise a method for continuous analytemonitoring. The method comprises receiving as an input an insertionlocation on a body where a sensor of a continuous analyte monitoringsystem was inserted into skin of a host. The method further comprisesstoring the insertion location. The method further comprises receivingas inputs data relating to a sensor session of the sensor. The methodfurther comprises correlating the data with the insertion location.

In an embodiment of the eighth aspect, the method further comprisesproviding a recommendation for a next insertion location based on thecorrelated data.

In an embodiment of the eighth aspect, the method further comprisesstoring the correlated data.

In an embodiment of the eighth aspect, the method further comprisestransmitting the correlated data to a database, the database includingother correlated data associated with other hosts.

In an embodiment of the eighth aspect, a remote computing deviceperforms at least one of the steps of storing the insertion location,receiving as inputs data relating to a sensor session of the sensor, andcorrelating the data with the insertion location.

In an embodiment of the eighth aspect, the data pertains to at least oneof sensor accuracy, sensor session length, sensor baseline, sensorsensitivity, sensor sensitivity decline over time, sensor performancevs. past performance in a same person, sensor performance vs. apopulation of users, sensor performance by days or wear, sensorperformance by geographical location, sensor performance by externalenvironment, sensor performance by activity level, data indicative ofsignal noise, time spent out of communication range, adhesive data,reliability, data capture, noise metrics, detected faults, end of lifemetrics, and confidence levels.

In an embodiment of the eighth aspect, the method further comprisesreceiving as an input personal information of the host.

In an embodiment of the eighth aspect, the personal information includesat least one of height, weight, age, sex, body mass index (BMI), theuser's current mood, the user's current pain level, the user's currentcomfort level, the user's current confidence level, the user'sperception of sensor performance, a location of an insulin infusion pumprelative to the sensor, adhesive irritation, or adhesive success rate.

In a ninth aspect, which is generally applicable (i.e. independentlycombinable with any of the aspects or embodiments identified herein),particularly with any other embodiment of the ninth aspect, certain ofthe present embodiments comprise a device substantially as shown and/ordescribed in the specification and/or drawings.

In a tenth aspect, which is generally applicable (i.e. independentlycombinable with any of the aspects or embodiments identified herein),particularly with any other embodiment of the tenth aspect, certain ofthe present embodiments comprise a method substantially as shown and/ordescribed in the specification and/or drawings.

In an eleventh aspect, which is generally applicable (i.e. independentlycombinable with any of the aspects or embodiments identified herein),particularly with any other embodiment of the eleventh aspect, certainof the present embodiments comprise a system substantially as shownand/or described in the specification and/or drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The present embodiments now will be discussed in detail with an emphasison highlighting the advantageous features. These embodiments depict thenovel and non-obvious indicator and analytics for sensor insertion in acontinuous analyte monitoring system and related methods shown in theaccompanying drawings, which are for illustrative purposes only. Thesedrawings include the following figures, in which like numerals indicatelike parts:

FIG. 1 is a schematic view of a continuous analyte sensor systemattached to a host and communicating with other devices;

FIG. 2A is a front elevation view of an electronic device configured foruse with the present systems and methods;

FIG. 2B is a functional block diagram of the electronic device of FIG.2A;

FIGS. 3-6 are front elevation views of the electronic device of FIG. 2A,illustrating various embodiments of a user interface;

FIGS. 7-14 are flowcharts illustrating various embodiments of methodsfor continuous analyte monitoring; and

FIG. 15 is a functional block diagram for a continuous analytemonitoring system including features described herein.

DETAILED DESCRIPTION

The following detailed description describes the present embodimentswith reference to the drawings. In the drawings, reference numbers labelelements of the present embodiments. These reference numbers arereproduced below in connection with the discussion of the correspondingdrawing features.

The preferred embodiments relate to the use of an analyte sensor thatmeasures a concentration of glucose or a substance indicative of theconcentration or presence of the analyte. In some embodiments, theanalyte sensor is a continuous device, for example a subcutaneous,transdermal, transcutaneous, and/or intravascular (e.g., intravenous)device. In some embodiments, the device can analyze a plurality ofintermittent blood samples. The analyte sensor can use any method ofglucose-measurement, including enzymatic, chemical, physical,electrochemical, optical, optochemical, fluorescence-based,spectrophotometric, spectroscopic (e.g., optical absorptionspectroscopy, Raman spectroscopy, etc.), polarimetric, calorimetric,iontophoretic, radiometric, and the like.

The analyte sensor can use any known method, including invasive,minimally invasive, and non-invasive sensing techniques, to provide adata stream indicative of the concentration of the analyte in a host.The data stream is typically a raw data signal that is used to provide auseful value of the analyte to a user, such as a patient or health careprofessional (e.g., doctor), who may be using the sensor.

Although much of the description and examples are drawn to a glucosesensor, the systems and methods of the preferred embodiments can beapplied to any measurable analyte. In some preferred embodiments, theanalyte sensor is a glucose sensor capable of measuring theconcentration of glucose in a host. One example embodiment is describedbelow, which utilizes an implantable glucose sensor. However, it shouldbe understood that the devices and methods described herein can beapplied to any device capable of detecting a concentration of analyteand providing an output signal that represents the concentration of theanalyte.

In one preferred embodiment, the analyte sensor is an implantableglucose sensor, such as described with reference to U.S. Pat. No.6,001,067 and U.S. Publ. No. 2011-0027127-A1. In another preferredembodiment, the analyte sensor is a transcutaneous glucose sensor, suchas described with reference to U.S. Publ. No. 2006-0020187-A1. In yetanother preferred embodiment, the analyte sensor is a dual electrodeanalyte sensor, such as described with reference to U.S. Publ. No.2009-0137887-A1. In still other embodiments, the sensor is configured tobe implanted in a host vessel or extracorporeally, such as is describedin U.S. Publ. No. 2007-0027385-A1.

The term “analyte” as used herein is a broad term, and is to be givenits ordinary and customary meaning to a person of ordinary skill in theart (and it is not to be limited to a special or customized meaning),and refers without limitation to a substance or chemical constituent ina biological fluid (for example, blood, interstitial fluid, cerebralspinal fluid, lymph fluid or urine) that can be analyzed. Analytes mayinclude naturally occurring substances, artificial substances,metabolites, and/or reaction products. In some embodiments, the analytefor measurement by the sensor heads, devices, and methods disclosedherein is glucose. However, other analytes are contemplated as well,including but not limited to lactate or lactic acid; cardiac markers;ketone bodies; acetone; acetoacetic acid; beta hydroxybutyric acid;glucagon, acetyl Co A; intermediaries in the Citric Acid Cycle; choline,testosterone; creatinine; triglycerides; sodium; potassium; chloride;bicarbonate; total protein; alkaline phosphatase; calcium; phosphorus;PO₂; PCO₂; bilirubin (direct and total); red blood cell count; whiteblood cell count; hemoglobin; hemactocrit; lymphocytes; monocytes;eosinophils; basophils; c-reactive protein; cryoglobulins; fibrinogens;ACTH; aldosterone; ammonia; beta-HCG; magnesium; copper; iron; totalcholesterol; low density lipoproteins; high density lipoproteins;lipoprotein A; T4 (total and free); TSH; FSH; LH; ACTH; hepatitis BEantigen; hepatitis B surface antigen; hepatitis A antibody; hepatitis Cantibody; acarboxyprothrombin; acylcarnitine; adenine phosphoribosyltransferase; adenosine deaminase; albumin; alpha-fetoprotein; amino acidprofiles (arginine (Krebs cycle), histidine/urocanic acid, homocysteine,phenylalanine/tyrosine, tryptophan); andrenostenedione; antipyrine;arabinitol enantiomers; arginase; benzoylecgonine (cocaine);biotinidase; biopterin; c-reactive protein; carnitine; carnosinase; CD4;ceruloplasmin; chenodeoxycholic acid; chloroquine; cholesterol;cholinesterase; conjugated 1-β hydroxy-cholic acid; cortisol; creatinekinase; creatine kinase MM isoenzyme; cyclosporin A; d-penicillamine;de-ethylchloroquine; dehydroepiandrosterone sulfate; DNA (acetylatorpolymorphism, alcohol dehydrogenase, alpha 1-antitrypsin, cysticfibrosis, Duchenne/Becker muscular dystrophy, analyte-6-phosphatedehydrogenase, hemoglobinopathies A, S, C, and E, D-Punjab,beta-thalassemia, hepatitis B virus, HCMV, HIV-1, HTLV-1, Leberhereditary optic neuropathy, MCAD, RNA, PKU, Plasmodium vivax, sexualdifferentiation, 21-deoxycortisol); desbutylhalofantrine;dihydropteridine reductase; diphtheria/tetanus antitoxin; erythrocytearginase; erythrocyte protoporphyrin; esterase D; fattyacids/acylglycines; free β-human chorionic gonadotropin; freeerythrocyte porphyrin; free thyroxine (FT4); free tri-iodothyronine(FT3); fumarylacetoacetase; galactose/gal-1-phosphate;galactose-1-phosphate uridyltransferase; gentamicin; analyte-6-phosphatedehydrogenase; glutathione; glutathione perioxidase; glycocholic acid;glycosylated hemoglobin; halofantrine; hemoglobin variants;hexosaminidase A; human erythrocyte carbonic anhydrase I; 17alpha-hydroxyprogesterone; hypoxanthine phosphoribosyl transferase;immunoreactive trypsin; lactate; lead; lipoproteins ((a), B/A-1, β);lysozyme; mefloquine; netilmicin; phenobarbitone; phenytoin;phytanic/pristanic acid; progesterone; prolactin; prolidase; purinenucleoside phosphorylase; quinine; reverse tri-iodothyronine (rT3);selenium; serum pancreatic lipase; sissomicin; somatomedin C; specificantibodies (adenovirus, anti-nuclear antibody, anti-zeta antibody,arbovirus, Aujeszky's disease virus, dengue virus, Dracunculusmedinensis, Echinococcus granulosus, Entamoeba histolytica, enterovirus,Giardia duodenalisa, Helicobacter pylori, hepatitis B virus, herpesvirus, HIV-1, IgE (atopic disease), influenza virus, Leishmaniadonovani, leptospira, measles/mumps/rubella, Mycobacterium leprae,Mycoplasma pneumoniae, Myoglobin, Onchocerca volvulus, parainfluenzavirus, Plasmodium falciparum, poliovirus, Pseudomonas aeruginosa,respiratory syncytial virus, rickettsia (scrub typhus), Schistosomamansoni, Toxoplasma gondii, Trepenoma pallidium, Trypanosomacruzi/rangeli, vesicular stomatis virus, Wuchereria bancrofti, yellowfever virus); specific antigens (hepatitis B virus, HIV-1);succinylacetone; sulfadoxine; theophylline; thyrotropin (TSH); thyroxine(T4); thyroxine-binding globulin; trace elements; transferrin;UDP-galactose-4-epimerase; urea; uroporphyrinogen I synthase; vitamin A;white blood cells; and zinc protoporphyrin. Salts, sugar, protein, fat,vitamins, and hormones naturally occurring in blood or interstitialfluids may also constitute analytes in certain embodiments. The analytemay be naturally present in the biological fluid, for example, ametabolic product, a hormone, an antigen, an antibody, and the like.Alternatively, the analyte may be introduced into the body, for example,a contrast agent for imaging, a radioisotope, a chemical agent, afluorocarbon-based synthetic blood, or a drug or pharmaceuticalcomposition, including but not limited to insulin; ethanol; cannabis(marijuana, tetrahydrocannabinol, hashish); inhalants (nitrous oxide,amyl nitrite, butyl nitrite, chlorohydrocarbons, hydrocarbons); cocaine(crack cocaine); stimulants (amphetamines, methamphetamines, Ritalin,Cylert, Preludin, Didrex, PreState, Voranil, Sandrex, Plegine);depressants (barbituates, methaqualone, tranquilizers such as Valium,Librium, Miltown, Serax, Equanil, Tranxene); hallucinogens(phencyclidine, lysergic acid, mescaline, peyote, psilocybin); narcotics(heroin, codeine, morphine, opium, meperidine, Percocet, Percodan,Tussionex, Fentanyl, Darvon, Talwin, Lomotil); designer drugs (analogsof fentanyl, meperidine, amphetamines, methamphetamines, andphencyclidine, for example, Ecstasy); anabolic steroids; and nicotine.The metabolic products of drugs and pharmaceutical compositions are alsocontemplated analytes. Analytes such as neurochemicals and otherchemicals generated within the body may also be analyzed, such as, forexample, ascorbic acid, uric acid, dopamine, noradrenaline,3-methoxytyramine (3MT), 3,4-dihydroxyphenylacetic acid (DOPAC),homovanillic acid (HVA), 5-hydroxytryptamine (5HT), and5-hydroxyindoleacetic acid (FHIAA).

For illustrative purposes, reference will now be made to FIG. 1, whichis an example environment in which some embodiments described herein maybe implemented. Here, an analyte monitoring system 100 includes acontinuous analyte sensor system 8. Continuous analyte sensor system 8includes a sensor electronics module 12 and a continuous analyte sensor10. The system 100 can also include other devices and/or sensors, suchas a medicament delivery pump 2 and a reference analyte meter 4, asillustrated in FIG. 1. The continuous analyte sensor 10 may bephysically connected to sensor electronics module 12 and may be integralwith (e.g., non-releasably attached to) or releasably attachable to thecontinuous analyte sensor 10. Alternatively, the continuous analytesensor 10 may be physically separate to sensor electronics module 12,but electronically coupled via inductive coupling or the like. Further,the sensor electronics module 12, medicament delivery pump 2, and/oranalyte reference meter 4 may communicate with one or more additionaldevices, such as any or all of display devices 14, 16, 18, 20, and/orone or more wearable devices 21.

The system 100 of FIG. 1 also includes a cloud-based processor 22configured to analyze analyte data, medicament delivery data, and/orother patient related data provided over network 24 directly orindirectly from one or more of sensor system 8, medicament delivery pump2, reference analyte meter 4, display devices 14, 16, 18, 20, andwearable device 21. Based on the received data, the processor 22 canfurther process the data, generate reports providing information basedon the processed data, trigger notifications to electronic devicesassociated with the host or caretaker of the host, or provide processedinformation to any of the other devices of FIG. 1. In some exampleimplementations, the cloud-based processor 22 comprises one or moreservers. If the cloud-based processor 22 comprises multiple servers, theservers can be either geographically local or separate from one another.The network 24 can include any wired and wireless communication mediumto transmit data, including WiFi networks, cellular networks, theInternet and any combinations thereof.

It should be understood that although the example implementationdescribed with respect to FIG. 1 refers to analyte data being receivedby processor 22, other types of data processed and raw data may bereceived as well.

In some example implementations, the sensor electronics module 12 mayinclude electronic circuitry associated with measuring and processingdata generated by the continuous analyte sensor 10. This generatedcontinuous analyte sensor data may also include algorithms, which can beused to process and calibrate the continuous analyte sensor data,although these algorithms may be provided in other ways as well. Thesensor electronics module 12 may include hardware, firmware, software,or a combination thereof to provide measurement of levels of the analytevia a continuous analyte sensor, such as a continuous glucose sensor.

The sensor electronics module 12 may, as noted, couple (e.g., wirelesslyand the like) with one or more devices, such as any or all of displaydevices 14, 16, 18, 20, and wearable device 21. The display devices 14,16, 18, 20 may be configured for processing and presenting information,such as sensor information transmitted by the sensor electronics module12 for display at the display device. The display devices 14, 16, 18,20, and/or the wearable device 21 may also trigger alarms based on theanalyte sensor data.

The wearable device 21 may also be configured for processing andpresenting information, such as sensor information transmitted by thesensor electronics module 12. The wearable device 21 may include analert interface. The alert interface may comprise, for example, adisplay, a vibration module, a shock module, a speaker, and/or any othertype of device that is capable of providing the user with physiologicalinformation.

In FIG. 1, display device 14 is a key fob-like display device, displaydevice 16 is a hand-held application-specific computing device 16 (e.g.the DexCom G4® Platinum receiver commercially available from DexCom,Inc.), display device 18 is a general purpose smart phone or tabletcomputing device 20 (e.g. an Apple® iPhone®, iPad®, or iPod Touch®commercially available from Apple, Inc.), display device 20 is acomputer workstation 20, and wearable device 21 is any device that isworn on, or integrated into, a user's vision, clothes, and/or bodies. Insome example implementations, the relatively small, key fob-like displaydevice 14 may be a computing device embodied in a wrist watch, a belt, anecklace, a pendent, a piece of jewelry, an adhesive patch, a pager, akey fob, a plastic card (e.g., credit card), an identification (ID)card, and/or the like. In some example implementations, the wearabledevice 21 may comprise anklets, glasses, rings, necklaces, arm bands,pendants, belt clips, hair clips/ties, pins, cufflinks, tattoos,stickers, socks, sleeves, gloves, garments (e.g. shirts, pants,underwear, bra, etc.), “clothing jewelry” such as zipper pulls, buttons,watches, shoes, contact lenses, subcutaneous implants, cochlearimplants, shoe inserts, braces (mouth), braces (body), medicalwrappings, sports bands (wrist band, headband), hats, bandages, hairweaves, nail polish, artificial joints/body parts, orthopedicpins/devices, implantable cardiac or neurological devices, etc. Thesmall display device 14 and/or the wearable device 21 may include arelatively small display (e.g., smaller than the display device 18) andmay be configured to display graphical and/or numerical representationsof sensor information, such as a numerical value 26 and/or an arrow 28.In contrast, the display devices 16, 18, and 20 can be larger displaydevices that can be capable of displaying a larger set of displayableinformation, such as a trend graph 30 depicted on the hand-held receiver16 in addition to other information such as a numerical value and arrow.

In various embodiments, the wearable device 21 may be attached to thewearer and/or to his or her clothing in any convenient fashion. Forexample, the wearable device 21 may encompass a body part of the wearer,such as an arm, a leg, the neck, etc. Instead, or in addition, thewearable device 21 may be secured to the wearer's skin with adhesive. Inembodiments including a vibration module, a shock module, or any otherdevice that provides the wearer with tactile feedback, these embodimentsmay be most effective if the wearable device 21 is directly orindirectly touching the wearer's skin in such a way that vibrations,shocks, etc. can be felt by the wearer. For example, directly securingthe wearable device 21 to the wearer's skin with adhesive may beadvantageous.

It is understood that any other user equipment (e.g. computing devices)configured to at least present information (e.g., a medicament deliveryinformation, discrete self-monitoring analyte readings, heart ratemonitor, caloric intake monitor, and the like) can be used in additionor instead of those discussed with reference to FIG. 1.

In some example implementations of FIG. 1, the continuous analyte sensor10 comprises a sensor for detecting and/or measuring analytes, and thecontinuous analyte sensor 10 may be configured to continuously detectand/or measure analytes as a non-invasive device, a subcutaneous device,a transdermal device, and/or an intravascular device. In some exampleimplementations, the continuous analyte sensor 10 may analyze aplurality of intermittent blood samples, although other analytes may beused as well.

In some example implementations of FIG. 1, the continuous analyte sensor10 may comprise a glucose sensor configured to measure glucose in theblood using one or more measurement techniques, such as enzymatic,chemical, physical, electrochemical, spectrophotometric, polarimetric,calorimetric, iontophoretic, radiometric, immunochemical, and the like.In implementations in which the continuous analyte sensor 10 includes aglucose sensor, the glucose sensor may be comprise any device capable ofmeasuring the concentration of glucose and may use a variety oftechniques to measure glucose including invasive, minimally invasive,and non-invasive sensing techniques (e.g., fluorescent monitoring), toprovide a data, such as a data stream, indicative of the concentrationof glucose in a host. The data stream may be raw data signal, which isconverted into a calibrated and/or filtered data stream used to providea value of glucose to a host, such as a user, a patient, or a caretaker(e.g., a parent, a relative, a guardian, a teacher, a doctor, a nurse,or any other individual that has an interest in the wellbeing of thehost). Moreover, the continuous analyte sensor 10 may be implanted as atleast one of the following types of sensors: an implantable glucosesensor, a transcutaneous glucose sensor, implanted in a host vessel orextracorporeally, a subcutaneous sensor, a refillable subcutaneoussensor, an intravascular sensor.

In some implementations of FIG. 1, the continuous analyte sensor system8 includes a DexCom G4® Platinum glucose sensor and transmittercommercially available from DexCom, Inc., for continuously monitoring ahost's glucose levels.

A sensor of a CGM system is implanted through the skin of the host. Whenthe sensor has reached the limit of its lifespan, it is removed and anew sensor is inserted. A typical sensor lifespan is anywhere from a fewdays to one week or more. Users of CGM systems thus insert a new sensorsometimes as often as every few days. Because the sensor penetrates theskin, repeated sensor insertion in the same location can lead toscarring. Thus, it can be desirable to vary the location for each newsensor insertion. However, the effectiveness of the sensor may beimpacted by the location where it is implanted, because the compositionof the host's body varies from one location to another, includingfactors such as fat concentration, skin thickness, presence ofcapillaries, muscle tissue, body heat, skin perspiration at the site,scarring, blood flow, movement of the location, vascularization, musclemovement, exposure to external elements/temperature (e.g., wrist vs.abdomen), insulin pump infusion sites, tattoos, rash or other skinconditions such as eczema or psoriasis, etc. Daily activities may alsoaffect the insertion site, such as seatbelt location/usage, repetitivemovement (e.g., exercise-related or work-related movements), normalsedentary positions (e.g., yoga, sleeping or sitting positions), orpressure from external factors, such as a uniform. Thus, it would bedesirable to know what location(s) on a given host's body are mostlikely to achieve the best CGM results.

FIG. 2A illustrates one embodiment of an electronic device 200configured for use with the present systems and methods. The electronicdevice 200 includes a display 202 and one or more input/output (I/O)devices, such as one or more buttons 204 and/or switches 206. In theillustrated embodiment, the electronic device 200 is a smartphone, andthe display 202 comprises a touchscreen, which also functions as an I/Odevice. In other embodiments, the electronic device 200 may comprise adevice or devices other than a smartphone, such as a receiver of a CGMsystem, a smartwatch, a tablet computer, a mini-tablet computer, ahandheld personal data assistant (PDA), a game console, a multimediaplayer, a wearable device, such as those described above, a screen in anautomobile, etc. While the electronic device 200 is illustrated as asmartphone in the figures, the electronic device 200 can be any of theother electronic devices mentioned herein and/or incorporate thefunctionality of any or all of the other electronic devices, includingwherein some or all of the functionally is embodied on a remote server.

FIG. 2B is a block diagram of the electronic device 200 shown in FIG.2A, illustrating its functional components in accordance with someembodiments. The electronic device 200 includes the display 202 and oneor more input/output (“I/O”) device(s) 204, 206, as described above withrespect to FIG. 2A. The display 202 may be any device capable ofdisplaying output, such as an LCD or LED screen and others. Theinput/output (I/O) devices 202, 204, 206 may comprise, for example, akeyboard (not shown), one or more buttons 204, one or more switches 206,etc. In embodiments including a touchscreen, the display 202 alsofunctions as an I/O device.

The electronic device 200 further includes a processor 208 (alsoreferred to as a central processing unit (CPU)), a memory 210, a storagedevice 212, a transceiver 214, and may include other components ordevices (not shown). The memory 210 is coupled to the processor 208 viaa system bus or a local memory bus 216. The processor 208 may be, or mayinclude, one or more programmable general-purpose or special-purposemicroprocessors, digital signal processors (DSPs), programmablecontrollers, application specific integrated circuits (ASICs),programmable logic devices (PLDs), or the like, or a combination of suchhardware-based devices.

The memory 210 provides the processor 208 access to data and programinformation that is stored in the memory 210 at execution time.Typically, the memory 210 includes random access memory (RAM) circuits,read-only memory (ROM), flash memory, or the like, or a combination ofsuch devices.

The storage device 212 may comprise one or more internal and/or externalmass storage devices, which may be or may include any conventionalmedium for storing large volumes of data in a non-volatile manner. Forexample, the storage device 212 may include conventional magnetic disks,optical disks, magneto-optical (MO) storage, flash-based storagedevices, or any other type of non-volatile storage devices suitable forstoring structured or unstructured data. The storage device 212 may alsocomprise storage in the “cloud” using so-called cloud computing. Cloudcomputing pertains to computing capability that provides an abstractionbetween the computing resource and its underlying technical architecture(e.g., servers, storage, networks), enabling convenient, on-demandnetwork access to a shared pool of configurable computing resources thatcan be rapidly provisioned and released with minimal management effortor service provider interaction.

The electronic device 200 may perform various processes, such as, forexample, correlating data, pattern analysis, and other processes. Insome embodiments, the electronic device 200 may perform such processeson its own. Alternatively, such processes may be performed by one ormore other devices, such as one or more cloud-based processors 22described above. In still further embodiments, these processes may beperformed in part by the electronic device 200 and in part by otherdevices. Various example processes are described herein with referenceto the electronic device 200. It should be understood that these exampleprocesses are not limited to being performed by the electronic device200 alone. Further, as used herein, the term “electronic device” shouldbe construed to include other devices with which the electronic device200 interacts, such as one or more cloud-based processors, servers, etc.

The electronic device 200 may also include other devices/interfaces forperforming various functions. For example, the electronic device 200 mayinclude a camera (not shown).

The transceiver 214 enables the electronic device 200 to communicatewith other computing systems, storage devices, and other devices via anetwork. While the illustrated embodiment includes a transceiver 214, inalternative embodiments a separate transmitter and a separate receivermay be substituted for the transceiver 214.

In some embodiments, the processor 208 may execute various applications,for example, a CGM application, which may be downloaded to theelectronic device 200 over the Internet and/or a cellular network, andthe like. Data for various applications may be shared between theelectronic device 200 and one or more other devices/systems, and storedby storage 212 and/or on one or more other devices/systems.

In certain of the present embodiments, the sensor 10 of the continuousanalyte sensor system 8 of FIG. 1 is inserted into the skin of a host. Anew sensor session is then initiated with the sensor 10, the sensorelectronics 12, and the electronic device 200. The embodiments describedherein contemplate numerous techniques for initializing the sensor 10.For example, initialization may be triggered when the sensor electronics12 engages the sensor 10. In another example, initialization may betriggered by a mechanical switch, such as a switch (not shown) on asnap-in base that receives the sensor electronics 12. When the sensorelectronics 12 are snapped into the base, the switch is automaticallytripped. In another example, initialization may be menu driven, as theuser may be prompted by a user interface on the display 202 of theelectronic device 200 to begin initialization by making a selection onthe user interface, such as by pushing a button or touching a designatedarea on a display 202 (which may comprise a touchscreen). In anotherexample involving a non-invasive sensor that is applied to the wearer'sskin, the sensor 10 may sense when it is in contact with skin and startautomatically. Further, the analyte sensor system 8 can detect use of anew sensor 10 using any of the above techniques, automatically promptthe user to confirm the new sensor session by way of a prompt on a userinterface of the system 8, and initiate an initialization response tothe user confirmation responsive to the prompt. Additional examples ofinitializing the sensor 10 are found in U.S. Publ. No. 2013-0245401-A1,the entire disclosure of which is hereby incorporated by referenceherein.

FIG. 3 illustrates an example of a user interface for tracking locationsof sensor insertion locations. When a new sensor session starts, theuser interface of FIG. 3 may be shown on the display 202. The presentembodiments contemplate numerous techniques for causing the userinterface of FIG. 3 to be shown on the display 202. For example, whenthe sensor electronics are powered on, the sensor electronics may send asignal to any electronic device within range of the sensor electronics,and the signal may prompt the electronic device to execute instructionsstored in the electronic device that cause the user interface of FIG. 3to appear on the display 202. In another example, the user may initiatecommunication between the sensor electronics and the electronic deviceby inputting one or more commands to the electronic device. For example,if the electronic device is a smartphone, and the executableinstructions stored in the electronic device comprise an application,such as a downloadable application, the user may launch the application.The user interface of FIG. 3 would then be shown on the display 202. Inanother example, the user interface of FIG. 3 may be shown on thedisplay 202 before a new sensor is inserted, such as when a previoussensor session ends, which may coincide with an expiration of theprevious sensor, or upon opening the CGM application when no sensor iscurrently inserted in the host and/or communicating with the CGMapplication.

As shown in FIG. 3, the user interface comprises a diagram 300 of abody. In the illustrated embodiment, both a front view 302 and a backview 304 of the body 300 are shown, but in alternative embodiments onlyone or the other may be shown. In some embodiments, the user may input,via the diagram 300, a location on the body where the sensor wasinserted. The electronic device 200 may then store the input location.For example, where the display 202 is a touchscreen, the user may tap onthe diagram 300 in the location on the body where the sensor wasinserted. The display 202 may also provide text 306, such asinstructions for the user. For example, in FIG. 3 the display 202indicates that the user should tap on (or otherwise indicate) thelocation on the body where the sensor was inserted.

In some embodiments, the display 202 may show a draggable icon of asensor, and the user may select the icon and drag it to the location onthe diagram 300 where the sensor was inserted. Also in some embodiments,a template may be provided, for example, inside packaging or printed onpackaging associated with a new sensor or another component of a CGMsystem. Alternatively, the template may be downloadable from a websiteon the Internet. The template may include a diagram of a body, which maybe similar to the diagram 300 discussed above. The diagram may havevarious sensor insertion sites identified, for example with label(s)and/or x-y coordinates. To input a location where a user has inserted anew sensor, the user may input into the electronic device the labeland/or coordinates of the insertion site as indicated on the diagram.

In certain embodiments, each time the user begins a new sensor session,he or she inputs into the electronic device the location of sensorinsertion, and the electronic device stores this information. Datapertaining to the sensor insertion locations may be used in manydifferent ways, according to the present embodiments. For example,quantitative data for each sensor session may be correlated with thesensor insertion location of each location. Quantitative data correlatedwith sensor insertion location may be, for example, sensor accuracy(e.g., a mean absolute relative difference (MARD) or other statisticalmeasure of accuracy), sensor session length, sensor baseline, sensorsensitivity, sensor sensitivity decline over time, sensor performancevs. past performance in the same person, sensor performance vs. a largerpopulation of users to figure out if a user is typical or atypical inhow the sensor performs in him or her, sensor performance by days orwear, sensor performance by geographical location, sensor performance byexternal environment (e.g. temperature, humidity), sensor performance byactivity level, data indicative of signal noise, time spent out ofcommunication range (e.g. because of a broken antenna), adhesive data(e.g. data obtained from user when an adhesive associated with thesensor falls off the skin), reliability, data capture, noise metrics,detected faults, end of life metrics, confidence levels, etc. Collecteddata may be compared with data from other users in a network based ondemographics, such as zip code, age, etc. Qualitative data may include arating system, number of starts (e.g., out of 5), thumbs up/down, userimpression, etc. The correlation may include consideration of additionalfactors, such as, for example, the user's body temperature, the user'ssleep cycle, what side the user sleeps on and/or how long the usersleeps on each side on average, or any other biometric data of the user.

With respect to sensor session length, although commercial sensors arelabeled for a predetermined sensor session duration (e.g., 3 days, 5days, 7 days, 10 days, etc), it is not uncommon for the useful life of acommercial sensor to end before the predetermined sensor sessionduration. After the useful life, the sensor may no longer be reliable,providing inaccurate sensor data. To prevent use beyond the useful life,some embodiments notify a user to change the sensor after it has beendetermined that the sensor should no longer be used. Various methods canbe used to determine whether a sensor should no longer be used, such asa predetermined amount of time transpiring since the sensor was firstused (e.g., when first implanted into a user or when first electricallyconnected to sensor electronics) or a determination that the sensor isdefective (e.g., due to membrane rupture, unstable sensitivity, or thelike). Once it is determined that the sensor should no longer be used,the sensor system can notify a user that a new sensor should be used byaudibly and/or visually prompting a user to use a new sensor and/orshutting down the display 202 or ceasing to display new (or real-time)sensor data on the display 202, for example. In some embodiments,continuous glucose monitors may show signs of sensor “end of life” neartheir end of life. The signs of end of life may be recognized and totalsensor end of life and any resulting user safety or inconvenience may beprevented.

The correlation may also or instead consider factors such as, forexample, any gaps or errors in sensor readings. Such gaps may resultfrom the user sleeping on the same side on which the sensor isimplanted, for example. U.S. patent application Ser. No. 13/836,260,filed on Mar. 15, 2013, at paragraphs 191-198, describes measuringimpedance to determine tissue compression, changes in the surroundingtissue, and/or properties of the tissue (e.g., % fat, hydration state,compression ischemia, etc.). Accordingly, the electronic device 200 maybe configured to correlate compression artifacts with sensor insertionlocation to determine whether a certain region of the patient (e.g.,left side of abdomen) correlates with the most compression artifacts(indicating a likelihood that the patient sleeps on the left side, forexample), wherein the electronic device 200 may then recommend adifferent region (e.g., right side abdomen) for sensor insertions.Similarly, the properties of the tissue at a particular location may notbe as well correlated with good sensor performance as compared to theproperties of the tissue at a different location, wherein the electronicdevice 200 may then recommend a particular region that has properties ofthe tissue correlated with good sensor performance. In another example,U.S. Publ. No. 2012-0265035-A1, at paragraphs 439-448, describes amethod of compensating for sensitivity changes based on measuredimpedance changes, and the specification as a whole describes systemsand methods for obtaining diagnostics data. The diagnostics data may beprocessed by the electronic device 200, to determine patterns thatassociate diagnostics data with sensor insertion location, wherein thepatterns may be used to identify and recommend sensor insertionlocations wherein the diagnostics data indicates good sensor reliabilityand/or performance. In another example, U.S. Pat. No. 8,423,113describes methods of detecting signal artifacts and replacing signalartifacts. Signal artifacts (e.g., levels of accuracy/confidence/signalartifacts) as described in U.S. Pat. No. 8,010,174 may be correlatedwith sensor performance, which data may be used to identify sensorinsertion locations with greater or lesser levels ofaccuracy/confidence/signal artifacts, etc. The entire disclosures of theforegoing (U.S. Publ. No. 2014-0005509-A1, U.S. Pat. No. 8,010,174, andU.S. Pat. No. 8,423,113) are hereby incorporated by reference herein.

The correlated data may be stored in the electronic device, such as instorage 212. The correlated data may also be transmitted to a database.The data may be transmitted anonymously, or may include identifyinginformation about the user. The database may include other correlateddata associated with other users. Information from the database may beprovided to various users. For example, the database may transmitinformation to a user who is about to begin a new sensor session. Theinformation may include one or more suggestions of sensor insertionlocations that have achieved good results for other users in thedatabase, where the good results may comprise high sensor accuracy, longsensor session length, comfort, completeness of data capture, or thelike.

The present embodiments contemplate numerous techniques for determiningsensor accuracy for a given sensor session. For example, U.S. PatentApplication Publication No. 2009/0192366, at paragraph 441, describes“an evaluation module, also referred to as the processor module, [that]evaluates a predictive accuracy of the calibration information. The term“predictive accuracy” refers without limitation to a measure of howaccurate or indicative calibration information is to a true correlationbetween the analyte signal and the actual analyte concentration, forexample, a measure of how well a matched data pair, a plurality ofmatched data pairs, a calibration set, and/or a calibration line willaccurately predict (i.e., estimate/correlate) glucose concentration froma sensor signal across a physiologically relevant range of glucoseconcentrations (e.g., between about 30 mg/dL and 600 mg/dL of glucoseconcentration).” In another example, U.S. Pat. No. 8,260,393, at column81, lines 56-64, describes “a self-diagnostics module, also referred toas a fail-safe module, [that] performs one or more calculations todetermine the accuracy, reliability, and/or clinical acceptability ofthe sensor data. Some examples of the self-diagnostics module aredescribed above, with reference block 556. The self-diagnostics modulecan be further configured to run periodically (e.g. intermittently or inresponse to a trigger), for example, on raw data, filtered data,calibrated data, predicted data, and the like.” In another example, U.S.Pat. No. 8,260,393, at column 79, lines 35-42, describes “theacceptability is determined by a quality evaluation, for example,calibration quality can be evaluated by determining the statisticalassociation of data that forms the calibration set, which determines theconfidence associated with the conversion function used in calibrationand conversion of raw sensor data into estimated analyte values. See,e.g. U.S. Publ. No. 2005-0027463-A1.” In another example, U.S. Publ. No.2013-0245401-A1, recites “[o]ne metric useful for determining much ofthe total error in the system is due to drift is to determine a ratio ofthe relative error (e.g., smoothed error) at calibration to the absoluteerror at calibration, and use an absolute value of that ratio for n (orto determine n). In the initial calculation of the ratio RelativeErrorand AbsoluteError may use seed values, after which the previous estimatemay be used in the following equations:RelativeError_(N)=Beta*ErrorAtCal+(1−Beta)*RelativeError_(N-1) andAbsoluteError_(N)=Beta*|ErrorAtCal|+(1−Beta)*AbsoluteError_(N-1). whereBeta is a forgetting factor for these equations.” In another example,sensor accuracy may be defined by a confidence in the sensor data basedon an end-of-life detection and/or outlier detection, as described inU.S. Publ. No. 2014-0182350-A1. In another example, sensor accuracy maybe defined by a level of certainty, as described in U.S. Publ. No.2014-0278189-A1. The entire disclosures of the foregoing (U.S. Publ. No.2009-0192366-A1, U.S. Pat. No. 8,260,393, US 2005-0027463-A1, U.S. Publ.No. 2013-0245401-A1, U.S. Publ. No. 2014-0182350-A1, and U.S. Publ. No.2014-0278189-A1) are hereby incorporated by reference herein.

In certain embodiments, a pattern analysis may be performed thatcorrelates quantitative data with each insertion location. Thequantitative data may embody any of the examples described above, forexample sensor accuracy and/or sensor session length. Pattern analysesmay be performed by the electronic device 200, by one or more otherdevices, such as one or more cloud-based processors/servers, and/or bythe electronic device 200 in conjunction with one or more other devices.

Pattern analyses may be presented to the user and/or stored in adatabase. The pattern analyses may be based on information specific tothe user, and/or based on information of other users in a database. Thepattern analyses may be used to make recommendations to the user aboutfuture sensor insertion locations, and/or to provide the user withfeedback. The pattern analyses may be presented to the user in variousformats, such as a ranked list, a graph, a histogram, a diagram of abody with a heat map, etc.

Any of a wide variety of algorithms that correlate and/or identifypatterns may be used with the embodiments described herein. Once thesystem has sufficient data about previous sensor insertion location (ofthe patient or a group of patients), pattern recognition may be used toidentify patterns or trends associated with insertion site locationsand/or may be used to recommend insertion site(s). Some examplemathematical approaches include regression, sequence labeling, parsing,neural network-based mapping, fuzzy logic-based pattern matching,Bayesian learning, and Genetic-Algorithms-based pattern matching.Additional considerations, such as a necessary rotation of insertionsites to avoid scarring, may also be included in determining arecommended next insertion site and/or rotation of insertion sites.

In certain embodiments, the electronic device may prompt the user toinput personal information. The personal information may include, forexample, at least one of height, weight, age, sex, body mass index(BMI), the user's current location, the user's current mood, the user'scurrent pain level, the user's current comfort level, the user's currentconfidence level, the user's perception of sensor performance, alocation of an insulin infusion pump relative to the sensor, adhesiveirritation, if any, adhesive success rate (when the adhesive started topeel or when the sensor fell off), how much daily insulin used by theuser, type of insulin, level of exercise/activity, current weather, etc.The electronic device may use the personal information to provide anadditional level of correlation between the sensor performance and theinsertion site. Based on the input data, the algorithm can suggest, forexample, that a 35 year old male should insert the sensor in theabdominal area, while a 45 year old woman should insert the sensor inthe triceps area.

Certain personal information, such as the user's confidence level,comfort level, mood, and overall perception of the sensor insertionprocess, might influence, or set the user's expectations for thatsensor's performance as well as future sensor performance. This data canbe beneficial for several reasons, including better understanding ofuser expectations, users can see if their wear experience was better orworse than their initial expectations, a CGM application can suggestinsertion sites with which the user may feel more comfortable, etc. Thisinformation can also be helpful in correlating user perceptions onaccuracy with actual sensor data measurements. Data relating to adhesiveissues can help identify issues with a certain lot of adhesive, whichissue(s) may be correlated by time of year, environmental factors, etc.

In certain embodiments, the electronic device may determine the user'sBMI based on information input by the user, such as his or her heightand weight. In alternative embodiments, the sensor and/or structureassociated with the sensor may include one or more electrodes formeasuring the user's BMI at the sensor insertion location.

In certain embodiments, the electronic device 200 and/or other devices,such as cloud-based devices, may store data pertaining to previoussensor sessions. For example, with reference to FIG. 4, the display 202may show a diagram 400 of a body that is annotated with data aboutprevious sensor locations 402. Each of the previous sensor locations 402may include textual information about that location, such as a date whena sensor was inserted at each location, quantitative data about thatlocation, and/or information about the user's activity level during eachsensor session. The quantitative data may embody any of the examplesdescribed above, for example sensor accuracy and/or sensor sessionlength. The information about previous sensor sessions may compriseinformation stored on the electronic device 200, and which is based onprevious sensor sessions for the user of that particular electronicdevice 200. Alternatively, or in addition, the information aboutprevious sensor sessions may comprise information downloaded to theelectronic device 200 from a database, which information is based uponprevious sensor sessions of users in the database, which may includeinformation about the user of that particular electronic device 200. Theinformation from the database may be organized by variouscharacteristics, such as age, sex, BMI, etc.

The textual information may be provided in one or more popup boxes 404,which may all appear on the screen at the same time, or which may appearone-by-one as the user selects successive ones of the previous sensorlocations 402. In certain embodiments, instead of or in addition to thetextual information, each previous sensor location 402 may begraphically categorized, such as color-coded. For example, locations 402that have been associated with good sensor performance in the past maybe a first color, such as green, while locations 402 that have beenassociated with bad sensor performance in the past may be a secondcolor, such as red. The present embodiments are, of course, not limitedto only two colors. Any number of colors and/or shades of the same colormay be used to indicate past sensor performance. The display 202 mayalso provide text 406, such as a description of what is being depicted.For example, in FIG. 4 the display 202 indicates that the user isviewing performance data regarding past sensor insertion locations.

Instead of, or in addition to, the functionality described above, theelectronic device 200 may track a user's sensor insertion locations inorder to provide the user with a suggested insertion location based on arecommended rotation of locations. For example, the user may bepresented a diagram 500 of a body on the display 202, as shown in FIG.5. The diagram 500 may include only one suggested sensor location 502,which location 502 is based on a recommended rotation of locations. Witheach successive sensor session, the electronic device 200 suggests thenext location 502 in the recommended rotation. The recommended rotationmay be based on information stored on the electronic device 200 and/orstored on one or more other devices, such as cloud-based servers. Thedisplay 202 may also provide text 504, such as a description of what isbeing depicted. For example, in FIG. 5 the display 202 indicates thatthe user is viewing a suggested sensor insertion location 502.

In certain embodiments, the electronic device may suggest a betterinsertion location based upon data from a current sensor session and/ordata of other users in a database. For example, during a sensor session,the system may determine that the sensor is not providing accurate data.The electronic device may prompt the user to remove the current sensorand implant a new one at a new insertion location indicated on thedisplay 202. The new insertion location may be associated with goodsensor performance in previous sensor sessions.

In certain embodiments, an application executing on the electronicdevice may show an outline of a body. A friend of the user, or the userwith the aid of a mirror, may then line up the template/outline of ahuman body over the user. A picture may then be taken to enable theapplication to know exactly where on the body the sensor is placed.

In certain embodiments, the electronic device may display every sensorinsertion location 600 in the recommended rotation, as shown in FIG. 6.Each of the insertion locations 600 may be consecutively numbered, and asuggested next insertion location may be highlighted. The display 202may also provide text 602, such as a description of what is beingdepicted. For example, in FIG. 6 the display 202 indicates that the useris viewing a recommended rotation of sensor insertion locations 600.

FIGS. 7-12 are flowcharts illustrating various embodiments of methodsfor continuous analyte monitoring. The various tasks performed inconnection with each one of the flowcharts of FIGS. 7-12 may beperformed by user action, by a processor executing instructions embodiedin a non-transitory computer-readable medium, or by a combination ofboth. For example, tasks may be performed by hardware, software,firmware, or any combination thereof, incorporated into one or more ofthe computing devices discussed herein. Any of the flowcharts of FIGS.7-12 may also include any number of additional or alternative tasks.Further, the tasks shown in FIGS. 7-12 need not be performed in theillustrated order and/or may be incorporated into a more comprehensiveprocedure or process having additional functionality not described indetail herein.

With reference to FIG. 7, some embodiments comprise, at block B700,inserting the sensor 10 of the continuous analyte sensor system 8 intoskin of a host. At block B702, the method further comprises initiating anew sensor session with the sensor electronics 12 and the device 200having the display 202. At block B704, the method further comprisesdisplaying the diagram 300 of a body on the display 202. At block B706,the method further comprises inputting, which may be via the diagram300, a location on the body where the sensor 10 was inserted. At blockB708, the method further comprises storing the location, such as storingthe location in the storage 212. At block B710, the method may furthercomprise correlating data from the sensor 10 for the sensor session withthe location. The correlating may be done by the electronic device 200,by other devices, such as the cloud-based processor 22 and/or otherdevices in the network 24, and/or by a combination thereof. At blockB712, the method may further comprise storing data, such as storing datain the storage 212. The data may be correlated data, data that has notbeen correlated, and/or a combination thereof. At block B714, the methodmay further comprise transmitting data (correlated or uncorrelated) to adatabase, such as a database in the network 24. The database may includedata associated with other hosts. The data may include quantitative dataregarding one or more sensor sessions corresponding to one or moresensor insertion locations. The quantitative data may embody any of theexamples described above, for example sensor accuracy and/or sensorsession length, correlated with each location. The correlation may bewith respect to two or more inputs. For example, another input may beavoiding scarring. At block B716, the method may further compriseinputting personal information of the host into the device 200. Thepersonal information may include, for example, at least one of height,weight, age, sex, body mass index (BMI), the user's current mood, theuser's current pain level, the user's current comfort level, the user'scurrent confidence level, the user's perception of sensor performance, alocation of an insulin infusion pump relative to the sensor, type ofinsulin, level of exercise/activity, adhesive irritation, if any,adhesive success rate (when the adhesive started to peel or when thesensor fell off), etc.

With reference to FIG. 8, some embodiments comprise, at block B800,initiating a new sensor session with the sensor electronics 12 and thedevice 200 having the display 202. At block B802, the method furthercomprises displaying a diagram 300 of a body on the display 202. Atblock B804, the method further comprises receiving as an input, whichmay be via the diagram 300, a location on the body where the sensor 10was inserted into skin of a host. At block B806, the method furthercomprises storing the location, such as storing the location in thestorage 212. At block B808, the method may further comprise correlatingquantitative data from the sensor 10 (such as the examples ofquantitative data described above) for the sensor session with thelocation. The correlating may be done by the electronic device 200, byother devices, such as the cloud-based processor 22 and/or other devicesin the network 24, and/or by a combination thereof. At block B810, themethod may further comprise storing data, such as storing data in thestorage 212. The data may be correlated data, data that has not beencorrelated, and/or a combination thereof. At block B812, the method mayfurther comprise transmitting data (correlated or uncorrelated) to adatabase, such as a database in the network 24. The database may includedata associated with other hosts. The data may include quantitative dataregarding one or more sensor sessions corresponding to one or moresensor insertion locations. The quantitative data may include any of theexamples of quantitative data described above correlated with eachlocation. The correlation may be with respect to two or more inputs. Forexample, another input may be avoiding scarring. At block B814, themethod may further comprise receiving as an input personal informationof the host. The personal information may include, for example, at leastone of height, weight, age, sex, body mass index (BMI), the user'scurrent mood, the user's current pain level, the user's current comfortlevel, the user's current confidence level, the user's perception ofsensor performance, a location of an insulin infusion pump relative tothe sensor, adhesive irritation, if any, adhesive success rate (when theadhesive started to peel or when the sensor fell off), etc.

With reference to FIG. 9, some embodiments comprise, at block B900,inserting the sensor 10 of the continuous analyte sensor system 8 intoskin of a host. At block B902, the method further comprises initiating anew sensor session with the sensor electronics 12 and the device 200having the display 202 and a camera. At block B904, the method furthercomprises photographing a location on the body where the sensor 10 wasinserted. For example, the photograph may be taken with a camera of thedevice 200. At block B906, the method further comprises storing thelocation, such as storing the location in the storage 212. At blockB908, the method may further comprise correlating quantitative data fromthe sensor 10 (such as the examples of quantitative data describedabove) for the sensor session with the location. The correlating may bedone by the electronic device 200, by other devices, such as thecloud-based processor 22 and/or other devices in the network 24, and/orby a combination thereof. At block B910, the method may further comprisestoring data, such as storing data in the storage 212. The data may becorrelated data, data that has not been correlated, and/or a combinationthereof. At block B912, the method may further comprise transmittingdata (correlated or uncorrelated) to a database, such as a database inthe network 24. The database may include data associated with otherhosts. The data may include quantitative data regarding one or moresensor sessions corresponding to one or more sensor insertion locations.The quantitative data may include any sensor information describedelsewhere herein, such as sensor accuracy correlated with each location,and/or sensor session length correlated with each location. Thecorrelation may be with respect to two or more inputs. For example,another input may be avoiding scarring. At block B914, the method mayfurther comprise inputting personal information of the host into thedevice 200. The personal information may include, for example, at leastone of height, weight, age, sex, body mass index (BMI), the user'scurrent mood, the user's current pain level, the user's current comfortlevel, the user's current confidence level, the user's perception ofsensor performance, a location of an insulin infusion pump relative tothe sensor, adhesive irritation, if any, adhesive success rate (when theadhesive started to peel or when the sensor fell off), etc.

With reference to FIG. 10, some embodiments comprise, at block B1000,initiating a new sensor session with the sensor electronics 12 and thedevice 200 having the display 202 and a camera. At block B1002, themethod further comprises receiving as an input, via the camera, aphotograph of a location on a body where the sensor 10 was inserted intoskin of a host. In some embodiments, the location may be determined bydigital signal analysis performed on the photograph. At block B1004, themethod further comprises storing the location, such as storing thelocation in the storage 212. At block B1006, the method may furthercomprise correlating data from the sensor 10 for the sensor session withthe location. The correlating may be done by the electronic device 200,by other devices, such as the cloud-based processor 22 and/or otherdevices in the network 24, and/or by a combination thereof. At blockB1008, the method may further comprise storing data, such as storingdata in the storage 212. The data may be correlated data, data that hasnot been correlated, and/or a combination thereof. At block B1010, themethod may further comprise transmitting data (correlated oruncorrelated) to a database, such as a database in the network 24. Thedatabase may include data associated with other hosts. The data mayinclude quantitative data regarding one or more sensor sessionscorresponding to one or more sensor insertion locations. Thequantitative data may include any of the examples described above, forexample sensor accuracy and/or sensor session length, correlated witheach location. The correlation may be with respect to two or moreinputs. For example, another input may be avoiding scarring. At blockB1012, the method may further comprise receiving as an input personalinformation of the host. The personal information may include, forexample, at least one of height, weight, age, sex, body mass index(BMI), the user's current mood, the user's current pain level, theuser's current comfort level, the user's current confidence level, theuser's perception of sensor performance, a location of an insulininfusion pump relative to the sensor, adhesive irritation, if any,adhesive success rate (when the adhesive started to peel or when thesensor fell off), etc.

With reference to FIG. 11, embodiments comprise, at block B1100,displaying, on the device 200 having the display 202, a diagram 400 of abody, the diagram indicating at least one location 402 on the body wherethe sensor 10 of the continuous analyte sensor system 8 was previouslyinserted. At block B1102, the method further comprises displaying, onthe display 202, information about each location 402. The informationmay include a date when the sensor 10 was inserted at each location 402.The information may include quantitative data regarding one or moresensor sessions corresponding to each sensor insertion location 402. Thequantitative data may include any of the examples described above, forexample sensor accuracy and/or sensor session length correlated witheach location 402.

With reference to FIG. 12, some embodiments comprise, at block B1200,downloading, to the device 200 having the display 202, data regardingone or more sensor sessions associated with a plurality of hosts, thedata being correlated with a location of each of the sensor sessions. Atblock B1202, the method further comprises displaying, on the display202, the correlated data. At block B1204, the method may furthercomprise providing a suggested sensor insertion location 502 on a bodyof a host, based on the correlated data. The correlated data may includeany of the examples of quantitative data described above, for examplesensor accuracy correlated with each location, and/or sensor sessionlength correlated with each location.

In some example embodiments, a sensor insertion site may be selectedbased on a particular fault mode, such as “dip and recover.” Thephenomenon known as “dip and recover” is a fault mode including asuppressed signal characteristic that is experienced by some patientsduring early sensor wear. This fault mode is described in detail in U.S.Publ. No. 2014-0005505-A1, which is incorporated herein by reference inits entirety and made a part of this disclosure. In this example, a usercan select an insertion site with the least likelihood of experiencingthe “dip and recover” phenomenon, which may be related to insertionsite. For example, a processor of an electronic device (such as theprocessor 208 of the electronic device 200) may query a database, suchas a database in the network 24, to determine a sensor insertion sitewith the least probability of experiencing “dip and recover.” Thedatabase may be patient-specific (e.g. contain data specific to thatuser), or the database may contain data pertaining to a population ofusers.

In some example embodiments, a sensor insertion site may be selectedbased on nighttime hypoglycemic reliability and/or accuracy. Forexample, a processor of an electronic device (such as the processor 208of the electronic device 200) may analyze data in the hypoglycemicrange, where the data is gathered during nighttime hours. The data maybe specific to that patient, or may pertain to a population of patients.Reliability and accuracy may be based on reliability metrics andaccuracy metrics, respectively.

In some example embodiments, the determination of sensor insertion sitelocation, and associated algorithmic analysis, may be optimized for aparticular patient use. For example, insertion site location andalgorithmic analysis may be optimized for time of day, hypoglycemicaccuracy, reduction of a particular fault mode, etc.

With reference to FIG. 13, some embodiments comprise, at block B1300,displaying, on the device 200 having the display 202, a diagram 300 of abody. At block B1302, the method further comprises indicating, on thediagram 300, a recommended location 600 for insertion of the sensor 10.At block B1304, the method may further comprise performing a patternanalysis of data relating to previous sensor insertion locations. Thedata may include data pertaining to at least one other user.

With reference to FIG. 14, some embodiments comprise, at block B1400,receiving as an input an insertion location on a body where the sensor10 of the continuous analyte sensor system 8 was inserted into skin of ahost. At block B1402, the method further comprises storing the insertionlocation, such as storing the insertion location in the storage 212. Atblock B1404, the method further comprises receiving as inputs datarelating to a sensor session of the sensor 10. At block B1406, themethod further comprises correlating the data with the insertionlocation. At block B1408, the method may further comprise providing arecommendation for a next insertion location 600 based on the correlateddata. At block B1410, the method may further comprise storing thecorrelated data, such as storing the correlated data in the storage 212.At block B1412, the method may further comprise transmitting thecorrelated data to a database, such as a database in the network 24. Thedatabase may include other correlated data associated with other hosts.

FIG. 15 is a functional block diagram for a continuous analytemonitoring system 1500 including features described herein. The system1500 may be implemented using any of the systems described herein,including any of those described with respect to FIGS. 1, 2A, 2B, and3-6. The system 1500 includes an input module 1502, which may be, forexample, any computer, any of the devices described herein that arecapable of receiving input, including any of the devices described withrespect to FIGS. 1, 2A, 2B, and 3-6, and/or be incorporated into any ofthese devices. The input module 1502 is configured to obtain any of theinputs described herein, including any behavior, context andphysiological inputs associated with a user. The inputs may be receivedfrom a user via manual input using a user interface, sensors (e.g.,biometric sensors, geographic positioning sensors, etc.), informationsystems (e.g. external health-data databases), social media, voiceresponse, actions on the system 1500 (e.g., search), and any otherinputs described herein. The input module 1502 may include wired (e.g.,Ethernet, USB, HDMI, coaxial cable, telephonic, patch cabling, fiberoptic cable) or wireless (e.g., WiFi, Bluetooth) communication channelsfor requesting and/or receiving the inputs.

In FIG. 15, the input module 1502 is coupled with an input processor1504 configured to process the received inputs. Processed inputs may bealso be added over time to a historical database 1510 for use in futureprocessing. The historical database 1510 is described in further detailbelow. The input processor 1504 may comprise, for example, a cloud-basedprocessor 22 such as that described with respect to FIG. 1, a processor208 such as that described with respect to FIG. 2B, or any otherprocessor. The input processor 1504 may be configured to process thereceived inputs based on processing rules obtained from a processingrules database 1506. The input processor 1504 may receive the input dataalong with the source of the data. Based on the source, a processingrule may be selected from the processing rules database 1506. Theprocessing rules may indicate a format for the input data, anappropriate parser, or other information to facilitate extraction andcategorization of the information included in the input data.

The input processor 1504 may provide the extracted information to one ormore engines 1508. The engines 1508 are configured to perform processingrelated to any of the processes described above. For example, one engine1508 may comprise a sensor performance tracking engine 1508A, which isconfigured to track sensor performance as described above with respectto FIGS. 3 and 4. Another engine 1508 may comprise an insertion locationsuggestion engine 1508B, which is configured to provide the user with asuggested location for insertion of a sensor based upon past sensorperformance in various insertion locations, as described above withrespect to FIG. 5. Another engine 1508 may comprise an insertionlocation rotation engine 1508C, which is configured to provide the userwith a suggested location for insertion of a sensor based upon a plannedrotation of insertion locations, as described above with respect to FIG.6.

Another engine 1508 may comprise a correlation engine 1508D, which isconfigured to perform the correlating described herein. For example, thecorrelation engine 1508D may perform the correlations described withrespect to boxes B710 (FIG. 7), B808 (FIG. 8), B908 (FIG. 9), B1006(FIG. 10), and B1406 (FIG. 14), as well as pattern analysis as discussedwith respect to B1304 of FIG. 13. Another engine 1508 may comprise aninsertion site recommendation engine 1508E, which is configured toperform the insertion site recommendations described herein. Forexample, the insertion site recommendation engine 1508E may perform theinsertion site recommendations described with respect to box B1204 (FIG.12).

The engines 1508 may be in data communication with a historical database1510. The historical database 1510 can include past input informationassociated with the user and/or aggregated information associated withother users (e.g., big data analytics for a community of similarlysituated users). Engines 1508 can use this information to perform thefunctions associated with each engine.

The system 1500 further includes an output module 1512. The outputmodule 1512 may receive outputs from the engines 1508 and display and/ortransmit the outputs. For example, the output can include insertionlocation suggestions to the user, and/or reports indicating performanceof insertion locations tracked by the system as discussed herein. Theoutput module 1512 may include a display and/or wired (e.g., Ethernet,USB, HDMI, coaxial cable, telephonic, patch cabling, fiber optic cable,etc.) and/or wireless (e.g., WiFi, Bluetooth, etc.) communication meansfor transmitting the outputs.

The connections between the elements shown in FIG. 15 illustrate examplecommunication paths for the system 1500. Additional communication paths,either direct or via an intermediary, may be included to furtherfacilitate the exchange of information for the system 1500. Thecommunication paths may be bi-directional communication paths allowingthe elements shown to exchange information.

Various implementations of the subject matter described herein may berealized in digital electronic circuitry, integrated circuitry,specially designed ASICs (application specific integrated circuits),computer hardware, firmware, software, and/or combinations thereof. Thecircuitry may be affixed to a printed circuit board (PCB), or the like,and may take a variety of forms, as noted. These various implementationsmay include implementation in one or more computer programs that areexecutable and/or interpretable on a programmable system including atleast one programmable processor, which may be special or generalpurpose, coupled to receive data and instructions from, and to transmitdata and instructions to, a storage system, at least one input device,and at least one output device.

These computer programs (also known as programs, software, softwareapplications, or code) include machine instructions for a programmableprocessor, and may be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the term “machine-readable medium” refers toany non-transitory computer program product, apparatus, and/or device(e.g., magnetic disks, optical disks, memory, programmable logic devices(PLDs)) used to provide machine instructions and/or data to aprogrammable processor, including a machine-readable medium thatreceives machine instructions.

While specific examples have been provided herein for illustrativepurposes, it is understood that to provide for interaction with a user,the subject matter described herein may be implemented on a computerhaving a display device (e.g., a CRT (cathode ray tube), LED(light-emitting diode), or LCD (liquid crystal display) monitor) fordisplaying information to the user and a keyboard and a pointing device(e.g., a mouse or a trackball) by which the user may provide input tothe computer. Other kinds of devices may be used to provide forinteraction with a user as well, for example, feedback provided to theuser may be any form of sensory feedback (e.g., visual feedback,auditory feedback, or tactile feedback), and input from the user may bereceived in any form, including acoustic, speech, or tactile input.

Further, while specific examples have been provided herein forillustrative purposes, it is understood that the subject matterdescribed herein may be implemented wholly or in part using a computingsystem that includes a back-end component (e.g., as a data server), orthat includes a middleware component (e.g., an application server), orthat includes a front-end component (e.g., a client computer having agraphical user interface or a Web browser through which a user mayinteract with an implementation of the subject matter described herein),or any combination of such back-end, middleware, or front-endcomponents. The components of the system may be interconnected by anyform or medium of digital data communication (e.g., a communicationnetwork). Examples of communication networks include a local areanetwork (“LAN”), a wide area network (“WAN”), and the Internet.

Although a few variations have been described in detail above, othermodifications are possible. For example, while the descriptions ofspecific implementations of the current subject matter discuss analyticapplications, the current subject matter is applicable to other types ofsoftware and data services access as well. Moreover, although the abovedescription refers to specific products, other products may be used aswell. In addition, the logic flows depicted in the accompanying figuresand described herein do not require the particular order shown, orsequential order, to achieve desirable results. Other implementationsmay be within the scope of the following claims.

It should be appreciated that all methods and processes disclosed hereinmay be used in any analyte monitoring system, continuous orintermittent. It should further be appreciated that the implementationand/or execution of all methods and processes may be performed by anysuitable devices or systems, whether local or remote. Further, anycombination of devices or systems may be used to implement the presentmethods and processes.

While the disclosure has been illustrated and described in detail in thedrawings and foregoing description, such illustration and descriptionare to be considered illustrative or exemplary and not restrictive. Thedisclosure is not limited to the disclosed embodiments. Variations tothe disclosed embodiments can be understood and effected by thoseskilled in the art in practicing the claimed disclosure, from a study ofthe drawings, the disclosure and the appended claims.

All references cited herein are incorporated herein by reference intheir entirety. To the extent publications and patents or patentapplications incorporated by reference contradict the disclosurecontained in the specification, the specification is intended tosupersede and/or take precedence over any such contradictory material.

Unless otherwise defined, all terms (including technical and scientificterms) are to be given their ordinary and customary meaning to a personof ordinary skill in the art, and are not to be limited to a special orcustomized meaning unless expressly so defined herein. It should benoted that the use of particular terminology when describing certainfeatures or aspects of the disclosure should not be taken to imply thatthe terminology is being re-defined herein to be restricted to includeany specific characteristics of the features or aspects of thedisclosure with which that terminology is associated. Terms and phrasesused in this application, and variations thereof, especially in theappended claims, unless otherwise expressly stated, should be construedas open ended as opposed to limiting. As examples of the foregoing, theterm ‘including’ should be read to mean ‘including, without limitation,’‘including but not limited to,’ or the like; the term ‘comprising’ asused herein is synonymous with ‘including,’ ‘containing,’ or‘characterized by,’ and is inclusive or open-ended and does not excludeadditional, unrecited elements or method steps; the term ‘having’ shouldbe interpreted as ‘having at least;’ the term ‘includes’ should beinterpreted as ‘includes but is not limited to;’ the term ‘example’ isused to provide exemplary instances of the item in discussion, not anexhaustive or limiting list thereof; adjectives such as ‘known’,‘normal’, ‘standard’, and terms of similar meaning should not beconstrued as limiting the item described to a given time period or to anitem available as of a given time, but instead should be read toencompass known, normal, or standard technologies that may be availableor known now or at any time in the future; and use of terms like‘preferably,’ ‘preferred,’ ‘desired,’ or ‘desirable,’ and words ofsimilar meaning should not be understood as implying that certainfeatures are critical, essential, or even important to the structure orfunction of the invention, but instead as merely intended to highlightalternative or additional features that may or may not be utilized in aparticular embodiment of the invention. Likewise, a group of itemslinked with the conjunction ‘and’ should not be read as requiring thateach and every one of those items be present in the grouping, but rathershould be read as ‘and/or’ unless expressly stated otherwise. Similarly,a group of items linked with the conjunction ‘or’ should not be read asrequiring mutual exclusivity among that group, but rather should be readas ‘and/or’ unless expressly stated otherwise.

Where a range of values is provided, it is understood that the upper andlower limit, and each intervening value between the upper and lowerlimit of the range is encompassed within the embodiments.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations may be expressly set forth herein for sakeof clarity. The indefinite article “a” or “an” does not exclude aplurality. A single processor or other unit may fulfill the functions ofseveral items recited in the claims. The mere fact that certain measuresare recited in mutually different dependent claims does not indicatethat a combination of these measures cannot be used to advantage. Anyreference signs in the claims should not be construed as limiting thescope.

It will be further understood by those within the art that if a specificnumber of an introduced claim recitation is intended, such an intentwill be explicitly recited in the claim, and in the absence of suchrecitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to embodiments containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should typically be interpreted to mean “atleast one” or “one or more”); the same holds true for the use ofdefinite articles used to introduce claim recitations. In addition, evenif a specific number of an introduced claim recitation is explicitlyrecited, those skilled in the art will recognize that such recitationshould typically be interpreted to mean at least the recited number(e.g., the bare recitation of “two recitations,” without othermodifiers, typically means at least two recitations, or two or morerecitations). Furthermore, in those instances where a conventionanalogous to “at least one of A, B, and C, etc.” is used, in generalsuch a construction is intended in the sense one having skill in the artwould understand the convention (e.g., “a system having at least one ofA, B, and C” would include but not be limited to systems that have Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). In those instances where aconvention analogous to “at least one of A, B, or C, etc.” is used, ingeneral such a construction is intended in the sense one having skill inthe art would understand the convention (e.g., “a system having at leastone of A, B, or C” would include but not be limited to systems that haveA alone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). It will be furtherunderstood by those within the art that virtually any disjunctive wordand/or phrase presenting two or more alternative terms, whether in thedescription, claims, or drawings, should be understood to contemplatethe possibilities of including one of the terms, either of the terms, orboth terms. For example, the phrase “A or B” will be understood toinclude the possibilities of “A” or “B” or “A and B.”

All numbers expressing quantities of ingredients, reaction conditions,and so forth used in the specification are to be understood as beingmodified in all instances by the term ‘about.’ Accordingly, unlessindicated to the contrary, the numerical parameters set forth herein areapproximations that may vary depending upon the desired propertiessought to be obtained. At the very least, and not as an attempt to limitthe application of the doctrine of equivalents to the scope of anyclaims in any application claiming priority to the present application,each numerical parameter should be construed in light of the number ofsignificant digits and ordinary rounding approaches.

Furthermore, although the foregoing has been described in some detail byway of illustrations and examples for purposes of clarity andunderstanding, it is apparent to those skilled in the art that certainchanges and modifications may be practiced. Therefore, the descriptionand examples should not be construed as limiting the scope of theinvention to the specific embodiments and examples described herein, butrather to also cover all modification and alternatives coming with thetrue scope and spirit of the invention.

What is claimed is:
 1. A method for continuous analyte monitoringincluding a continuous analyte monitoring system having a sensor coupledto a transmitter unit in wireless communication with a device having adisplay, the method comprising: initiating a sensor session of thesensor with the transmitter unit; displaying, using an input module ofthe device, a diagram of a body on the display; receiving as an input,via the diagram, a location on the body where the sensor was insertedinto skin of a host; storing the location; and correlating data from thesensor for the sensor session with the location using a correlationengine, wherein the data includes quantitative data regarding one ormore sensor sessions corresponding to one or more sensor insertionlocations, wherein the quantitative data comprises at least one ofsensor accuracy, sensor session length, sensor baseline, sensorsensitivity, sensor sensitivity decline over time, sensor performancevs. past performance in a same person, sensor performance vs. apopulation of users, sensor performance by days or wear, sensorperformance by geographical location, sensor performance by externalenvironment, sensor performance by activity level, data indicative ofsignal noise, time spent out of communication range, adhesive data,reliability, data capture, noise metrics, detected faults, end of lifemetrics, or confidence levels.
 2. The method of claim 1, furthercomprising storing the correlated data.
 3. The method of claim 1,further comprising transmitting the correlated data to a database, thedatabase including other correlated data associated with other hosts. 4.The method of claim 1, further comprising receiving as an input personalinformation of the host using the input module.
 5. The method of claim4, wherein the personal information includes at least one of height,age, sex, body mass index, the host's current mood, the host's currentpain level, the host's current comfort level, the host's currentconfidence level, the host's perception of sensor performance, alocation of an insulin infusion pump relative to the sensor, adhesiveirritation, or adhesive success rate.
 6. A method for continuous analytemonitoring including a continuous analyte monitoring system having asensor coupled to a transmitter unit in wireless communication with adevice having a display and a camera, the method comprising: initiatinga sensor session of the sensor with the transmitter unit; receiving asan input, via the camera of the device, a photograph of a location on abody where the sensor was inserted into skin of a host; analyzing thephotograph to determine the location using an input processor; storingthe location in a database; and correlating data from the sensor for thesensor session with the location using a correlation engine, wherein thedata includes quantitative data regarding one or more sensor sessionscorresponding to one or more sensor insertion locations, wherein thequantitative data comprises at least one of sensor accuracy, sensorsession length, sensor baseline, sensor sensitivity, sensor sensitivitydecline over time, sensor performance vs. past performance in a sameperson, sensor performance vs. a population of users, sensor performanceby days or wear, sensor performance by geographical location, sensorperformance by external environment, sensor performance by activitylevel, data indicative of signal noise, time spent out of communicationrange, adhesive data, reliability, data capture, noise metrics, detectedfaults, end of life metrics, or confidence levels.
 7. The method ofclaim 6, further comprising storing the correlated data.
 8. The methodof claim 6, further comprising transmitting the correlated data to thedatabase, the database including other correlated data associated withother hosts.
 9. The method of claim 4, further comprising using an inputmodule to receive personal information of the host.
 10. The method ofclaim 9, wherein the personal information includes at least one ofheight, weight, age, sex, body mass index, the host's current mood, thehost's current pain level, the host's current comfort level, the host'scurrent confidence level, the host's perception of sensor performance, alocation of an insulin infusion pump relative to the sensor, adhesiveirritation, or adhesive success rate.
 11. A method for continuousanalyte monitoring, comprising: generating, at a cloud computer,quantitative data associated with sensor data from an analyte sensorrelating to a sensor session of a host user, the generating includingcorrelating the sensor data from the analyte sensor for the sensorsession with a location on the host user's body where the analyte sensorwas inserted; receiving, at the cloud computer, analyzed populationsensor data associated with a population of continuous analytemonitoring system users, the analyze population sensor data includingcorrelated sensor data from an analyte sensor for each individual hostof the population obtained during a sensor session that is correlatedwith a location on the respective individual host's body where theanalyte sensor was inserted; producing, at the cloud computer, asuggested sensor insertion location on the host user's body for a nextsensor session based on processing the quantitative data with thereceived analyzed sensor data; wirelessly transmitting, from the cloudcomputer to a display device of the host user, the produced suggestedsensor insertion location; and displaying, on the display device, thesuggested sensor insertion location, wherein the quantitative datapertains to accuracy and/or reliability of the sensor data, thequantitative data comprising at least one of a sensor accuracy metric, asensor session length metric, a sensor baseline metric, a sensorsensitivity metric, a sensor sensitivity decline over time metric, or asensor performance metric.
 12. The method of claim 11, wherein thesensor performance metric includes one or more of sensor performance vs.past performance in a same person, sensor performance vs. pastperformance in a population of users, sensor performance by days orwear, sensor performance by geographical location, sensor performance byexternal environment, or sensor performance by activity level.
 13. Themethod of claim 11, wherein the quantitative data pertains to at leastone of signal noise, time spent out of communication range, adhesivedata, data capture, noise metrics, detected faults, end of life metrics,or confidence levels.